scholarly journals Exploiting Efficient Representations in Large-Scale Tensor Decompositions

2019 ◽  
Vol 41 (2) ◽  
pp. A789-A815 ◽  
Author(s):  
Nico Vervliet ◽  
Otto Debals ◽  
Lieven De Lathauwer
Keyword(s):  
Large Scale ◽  
Tensor Decompositions ◽  
Large Scale Tensor

scholarly journals Large scale tensor regression using kernels and variational inference

2021 ◽  
Author(s):  
Robert Hu ◽  
Geoff K. Nicholls ◽  
Dino Sejdinovic
Keyword(s):  
Large Scale ◽  
Side Information ◽  
Variational Inference ◽  
Superior Performance ◽  
Tensor Factorization ◽  
Latent Factors ◽  
Uncertainty Estimates ◽  
Novel Method ◽  
Prediction And Forecasting ◽  
Large Scale Tensor

AbstractWe outline an inherent flaw of tensor factorization models when latent factors are expressed as a function of side information and propose a novel method to mitigate this. We coin our methodology kernel fried tensor (KFT) and present it as a large-scale prediction and forecasting tool for high dimensional data. Our results show superior performance against LightGBM and Field aware factorization machines (FFM), two algorithms with proven track records, widely used in large-scale prediction. We also develop a variational inference framework for KFT which enables associating the predictions and forecasts with calibrated uncertainty estimates on several datasets.

Tensor Factorization and Clustering for the Feature Extraction Based on Tucker3 with Updating Core

2011 ◽  
Vol 308-310 ◽  
pp. 2517-2522 ◽  
Author(s):  
Hai Jun Wang ◽  
Fei Yun Xu ◽  
Fei Wang
Keyword(s):  
Feature Extraction ◽  
Parallel Computing ◽  
Large Scale ◽  
Tensor Factorization ◽  
Parallel Mode ◽  
Large Scale Tensor

Aiming at the problems of Tucker3 to large-scale tensor when applied to feature extraction, a new factorization based on Tucker3 is proposed to extract feature from the tensors. First, the large-scale tensor is divided into multiple sub-tensors so as to conveniently compute cores of sub-tensors in parallel mode with Matlab Parallel Computing Toolbox; Then, the cores of each sub-tensor are updated for reducing deviation in calculating and the similar characteristics of sub-tensors are clustered to obtain the features. Experiment results show that this methods is able to extract features rapidly and efficiently.

scholarly journals Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions

2016 ◽  
Vol 9 (4-5) ◽  
pp. 249-429 ◽  
Author(s):  
Andrzej Cichocki ◽  
Namgil Lee ◽  
Ivan Oseledets ◽  
Anh-Huy Phan ◽  
Qibin Zhao ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Large Scale ◽  
Low Rank ◽  
Large Scale Optimization ◽  
Tensor Decompositions ◽  
Tensor Networks ◽  
Rank Tensor ◽  
Scale Optimization
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